The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
In crop analytics and plant breeding, researchers often need to collect samples of a plant product, e.g., tissues, seeds, fiber, leaves, roots, etc. in order to assess and compare crop performance of certain germplasms growing in certain plots. When collecting samples, researchers aim to reduce errors in at least four different ways: 1) by ensuring identity preservation, i.e. that each plot is accurately identified and that the identity of the sample collected accurately corresponds to its respective plot identity (e.g. that the contents of each sample are labeled accurately and in a trackable way), 2) by ensuring that each sample is free from contamination from other plots, 3) by ensuring that the sample collected from a plot accurately reflects the characteristics that the plant product is likely to exhibit when produced in a commercial setting, and 4) by ensuring that all samples are collected in a uniform manner.
Current sample collection methods include sending teams of human collectors into the field to collect samples directly by hand or by using hand-held tools, but these approaches are fraught with issues. For one, human collectors occasionally misidentify plots and/or mislabel collected samples, leading to spurious data and inaccurate selection decisions. Also, the samples that humans collect tend to over represent the plant product produced at the easiest to reach places on a plant, which can lead to samples that do not reflect what a grower and/or customer is likely to experience under commercial settings, e.g., when using a mechanized commercial harvester. Furthermore, since it is very difficult for any single human to collect plant product samples from different plots in exactly the same way, it is easy to understand why variations in the way samples are collected are unavoidable when the same person is collecting several dozen samples at a time, and even more so as the number of samples a single person collects climbs to the hundreds or thousands in a day. This lack of sample collection uniformity is exacerbated in global industrial plant breeding programs which routinely attempt to compare the performance of plants based on the properties of plant product samples collected on different days by different people in different parts of the world. Under such considerations, the limitations of human collection teams reliably collecting uniform samples become clear.
For example, cotton bolls are known to mature at different rates depending on where they are on the plant, i.e., the bolls at the bottom of the plant will tend to be more mature, and thus exhibit different yield and quality, than those growing from the top of the plant. Generally, such boll maturity distribution does not matter to the grower because typical commercial-grade cotton harvesters reliably collect all the bolls on each plant, so the final assessment as to the value of the crop is based on the combined quality of cotton each entire plant produces. However, cotton breeders generally do not want the sample to contain cotton from all the plants in a plot for two reasons: 1) the cotton produced by plants growing near alleyways will not reflect the characteristics that that a grower should expect from that variety because those plants face less competition for light and other resources due to the presence of alleys at the end of each plot; and 2) occasional contamination is expected to occur between some adjacent plots in the same row because the end plants often become so overgrown that some overlap is bound to occur.
Additionally, in some crop species, including cotton, the true variation in performance between plants in a plot can be low enough that samples collected from one, two, three or more plants growing in the middle of a particular plot can be considered representative of that entire plot. For example, when breeders sample cotton from a plot to compare the performance of germplasms, often they will focus collecting samples from only those plants growing in the middle of each plot.
Therefore, known methods for collecting samples of a particular plant product, e.g., seed cotton samples, inherently produce experimental errors and inefficiently collect plant products, especially in research settings.